Department of Chemistry, School of Engineering Sciences in Chemistry, Biology and Health (CBH), KTH Royal Institute of Technology, 11428 Stockholm, Sweden.
Division of Robotics, Perception and Learning (RPL), School of Electrical Engineering and Computer Science (EECS), KTH Royal Institute of Technology, 10044 Stockholm, Sweden.
J Chem Inf Model. 2020 Mar 23;60(3):1302-1316. doi: 10.1021/acs.jcim.9b00945. Epub 2020 Mar 13.
We define a as a pair of molecules in which one molecule (the "host" or "cage") possesses a cavity that can encapsulate the other molecule (the "guest") and prevent it from escaping. Molecular caging complexes can be useful in applications such as molecular shape sorting, drug delivery, and molecular immobilization in materials science, to name just a few. However, the design and computational discovery of new caging complexes is a challenging task, as it is hard to predict whether one molecule can encapsulate another because their shapes can be quite complex. In this paper, we propose a computational screening method that predicts whether a given pair of molecules form a caging complex. Our method is based on a caging verification algorithm that was designed by our group for applications in robotic manipulation. We tested our algorithm on three pairs of molecules that were previously described in a pioneering work on molecular caging complexes and found that our results are fully consistent with the previously reported ones. Furthermore, we performed a screening experiment on a data set consisting of 46 hosts and four guests and used our algorithm to predict which pairs are likely to form caging complexes. Our method is computationally efficient and can be integrated into a screening pipeline to complement experimental techniques.
我们将一对分子定义为其中一个分子(“主体”或“笼”)具有一个可以容纳另一个分子(“客体”)并防止其逃逸的空腔。分子笼复合物在分子形状分类、药物输送、材料科学中的分子固定等应用中非常有用。然而,设计和计算发现新的笼复合物是一项具有挑战性的任务,因为很难预测一个分子是否可以封装另一个分子,因为它们的形状可能非常复杂。在本文中,我们提出了一种计算筛选方法,用于预测给定的一对分子是否形成笼复合物。我们的方法基于我们小组为机器人操作应用设计的笼验证算法。我们在三个分子对中测试了我们的算法,这三个分子对在前人关于分子笼复合物的开创性工作中已有描述,发现我们的结果与之前报道的结果完全一致。此外,我们在由 46 个主体和 4 个客体组成的数据集上进行了筛选实验,并使用我们的算法预测哪些对可能形成笼复合物。我们的方法计算效率高,可以集成到筛选管道中,以补充实验技术。